On Fri, Jun 10, 2011 at 5:19 PM, Olivier Delalleau <shish@keba.be> wrote:
> 2011/6/10 Charles R Harris <charlesr.harris@gmail.com>
>>>>>>> On Fri, Jun 10, 2011 at 3:43 PM, Benjamin Root <ben.root@ou.edu> wrote:
>>>>>>>>>>> On Fri, Jun 10, 2011 at 3:24 PM, Charles R Harris <
>>>charlesr.harris@gmail.com> wrote:
>>>>>>>>>>>>>>> On Fri, Jun 10, 2011 at 2:17 PM, Benjamin Root <ben.root@ou.edu> wrote:
>>>>>>>>>>>>>>>>>>> On Fri, Jun 10, 2011 at 3:02 PM, Charles R Harris <
>>>>>charlesr.harris@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>>>> On Fri, Jun 10, 2011 at 1:50 PM, Benjamin Root <ben.root@ou.edu>wrote:
>>>>>>>>>>>>> Came across an odd error while using numpy master. Note, my system
>>>>>>> is 32-bits.
>>>>>>>>>>>>>> >>> import numpy as np
>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32
>>>>>>> False
>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int64)) == np.int64
>>>>>>> True
>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.float32)) == np.float32
>>>>>>> True
>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.float64)) == np.float64
>>>>>>> True
>>>>>>>>>>>>>> So, only the summation performed with a np.int32 accumulator results
>>>>>>> in a type that doesn't match the expected type. Now, for even more
>>>>>>> strangeness:
>>>>>>>>>>>>>> >>> type(np.sum([1, 2, 3], dtype=np.int32))
>>>>>>> <type 'numpy.int32'>
>>>>>>> >>> hex(id(type(np.sum([1, 2, 3], dtype=np.int32))))
>>>>>>> '0x9599a0'
>>>>>>> >>> hex(id(np.int32))
>>>>>>> '0x959a80'
>>>>>>>>>>>>>> So, the type from the sum() reports itself as a numpy int, but its
>>>>>>> memory address is different from the memory address for np.int32.
>>>>>>>>>>>>>>>>>>>> One of them is probably a long, print out the typecode, dtype.char.
>>>>>>>>>>>> Chuck
>>>>>>>>>>>>>>>>>>>>>>> Good intuition, but odd result...
>>>>>>>>>> >>> import numpy as np
>>>>> >>> a = np.sum([1, 2, 3], dtype=np.int32)
>>>>> >>> b = np.int32(6)
>>>>> >>> type(a)
>>>>> <type 'numpy.int32'>
>>>>> >>> type(b)
>>>>> <type 'numpy.int32'>
>>>>> >>> a.dtype.char
>>>>> 'i'
>>>>> >>> b.dtype.char
>>>>> 'l'
>>>>>>>>>> So, the standard np.int32 is getting listed as a long somehow? To
>>>>> further investigate:
>>>>>>>>>>>>>> Yes, long shifts around from int32 to int64 depending on the OS. For
>>>> instance, in 64 bit Windows it's 32 bits while in 64 bit Linux it's 64 bits.
>>>> On 32 bit systems it is 32 bits.
>>>>>>>> Chuck
>>>>>>>>>>> Right, that makes sense. But, the question is why does sum() put out a
>>> result dtype that is not identical to the dtype that I requested, or even
>>> the dtype of the input array? Could this be an indication of a bug
>>> somewhere? Even if the bug is harmless (it was only noticed within the test
>>> suite of larry), is this unexpected?
>>>>>>>> I expect sum is using a ufunc and it acts differently on account of the
>> cleanup of the ufunc casting rules. And yes, a long *is* int32 on your
>> machine. On mine
>>>> In [4]: dtype('q') # long long
>> Out[4]: dtype('int64')
>>>> In [5]: dtype('l') # long
>> Out[5]: dtype('int64')
>>>> The mapping from C types to numpy width types isn't 1-1. Personally, I
>> think we should drop long ;) But it used to be the standard Python type in
>> the C API. Mark has also pointed out the problems/confusion this ambiguity
>> causes and someday we should probably think it out and fix it. But I don't
>> think it is the most pressing problem.
>>>> Chuck
>>>>> But isn't it a bug if numpy.dtype('i') != numpy.dtype('l') on a 32 bit
> computer where both are int32?
>>Maybe yes, maybe no ;) They have different descriptors, so from numpy's
perspective they are different, but at the hardware/precision level they are
the same. It's more of a decision as to what != means in this case. Since
numpy started as Numeric with only the c types the current behavior is
consistent, but that doesn't mean it shouldn't change at some point.
Chuck
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